def main():
# train_X = np.asarray([[0.2, -0.3]])
# train_Y = np.asarray([[0.0, 1.0, 0.0]])
train_X = np.asarray([{0: 0.45, 1: 3.33}, {1: 2.22}])
train_Y = np.asarray([{1: 1.0}, {2: 1.0}])
net = ParticleSparseNetwork(cost="mse_sparse", particle_input=ParticleSparseInput(2))
net.append(ParticleSparse(2, 3))
# net.append(ParticleSparse(5, 3))
print(net.particle_input.get_rxyz())
print(net.predict(train_X))
print(net.cost(train_X, train_Y))
print(net.cost_gradient(train_X, train_Y))
def fd():
train_X = np.asarray([{0: 0.45, 1: 3.33}, {1: 2.22}, {0: -0.9}])
# train_X = np.asarray([{0: 0.45, 1: 3.33}])
train_Y = np.asarray([{1: 1.0}, {2: 1.0}, {0: 1.0}])
net = ParticleSparseNetwork(cost="mse_sparse", particle_input=ParticleSparseInput(2))
net.append(ParticleSparse(2, 5, ktop=1))
net.append(ParticleSparse(5, 6))
net.append(ParticleSparse(6, 3))
# Finite difference checking
net.cost(train_X, train_Y)
db, dq, dr, dt = net.cost_gradient(train_X, train_Y)
h = 0.00001
print("analytic b")
print(db)
fd_b = []
for l in range(len(net.layers)):
lb = []
for c in range(len(net.layers[l].b)):
for b in range(len(net.layers[l].b[c])):
orig = net.layers[l].b[c][b]
net.layers[l].b[c][b] += h
fp = net.cost(train_X, train_Y)
net.layers[l].b[c][b] -= 2*h
fm = net.cost(train_X, train_Y)
lb.append((fp - fm) / (2*h))
net.layers[l].b[c][b] = orig
fd_b.append(lb)
print("numerical b")
print(fd_b)
print("analytic q")
for x in dq:
print(x)
fd_q = []
for l in range(len(net.layers)):
lq = []
for i in range(len(net.layers[l].q)):
orig = net.layers[l].q[i]
net.layers[l].q[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].q[i] -= 2*h
fm = net.cost(train_X, train_Y)
lq.append((fp - fm) / (2*h))
net.layers[l].q[i] = orig
fd_q.append(lq)
print("numerical q")
for x in fd_q:
print(x)
print("analytic theta")
for x in dt:
print(x)
# input layer
fd_t = []
layer = net.particle_input
lt = []
for i in range(len(layer.theta)):
orig = layer.theta[i]
layer.theta[i] += h
fp = net.cost(train_X, train_Y)
layer.theta[i] -= 2*h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2*h))
layer.theta[i] = orig
fd_t.append(lt)
# layers
for l in range(len(net.layers)):
lt = []
for i in range(len(net.layers[l].theta)):
orig = net.layers[l].theta[i]
net.layers[l].theta[i] += h
fp = net.cost(train_X, train_Y)
net.layers[l].theta[i] -= 2*h
fm = net.cost(train_X, train_Y)
lt.append((fp - fm) / (2*h))
net.layers[l].theta[i] = orig
fd_t.append(lt)
print("numerical theta")
for x in fd_t:
print(x)
fd_r_x = []
fd_r_y = []
fd_r_z = []
# input first
layer = net.particle_input
lr_x = []
lr_y = []
lr_z = []
for i in range(layer.output_size):
# x
orig = layer.rx[i]
layer.rx[i] += h
fp = net.cost(train_X, train_Y)
layer.rx[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_x.append((fp - fm) / (2*h))
layer.rx[i] = orig
# y
orig = layer.ry[i]
layer.ry[i] += h
fp = net.cost(train_X, train_Y)
layer.ry[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_y.append((fp - fm) / (2*h))
layer.ry[i] = orig
# z
orig = layer.rz[i]
layer.rz[i] += h
fp = net.cost(train_X, train_Y)
layer.rz[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_z.append((fp - fm) / (2*h))
layer.rz[i] = orig
fd_r_x.append(lr_x)
fd_r_y.append(lr_y)
fd_r_z.append(lr_z)
# layers
for layer in net.layers:
lr_x = []
lr_y = []
lr_z = []
for i in range(layer.output_size):
# x
orig = layer.rx[i]
layer.rx[i] += h
fp = net.cost(train_X, train_Y)
layer.rx[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_x.append((fp - fm) / (2*h))
layer.rx[i] = orig
# y
orig = layer.ry[i]
layer.ry[i] += h
fp = net.cost(train_X, train_Y)
layer.ry[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_y.append((fp - fm) / (2*h))
layer.ry[i] = orig
# z
orig = layer.rz[i]
layer.rz[i] += h
fp = net.cost(train_X, train_Y)
layer.rz[i] -= 2*h
fm = net.cost(train_X, train_Y)
lr_z.append((fp - fm) / (2*h))
layer.rz[i] = orig
fd_r_x.append(lr_x)
fd_r_y.append(lr_y)
fd_r_z.append(lr_z)
print("analytic x")
for layer in dr[0]:
print(layer)
print("numerical r x")
for f in fd_r_x:
print(f)
print("analytic y")
for layer in dr[1]:
print(layer)
print("numerical r y")
for f in fd_r_y:
print(f)
print("analytic z")
for layer in dr[2]:
print(layer)
print("numerical r z")
for f in fd_r_z:
print(f)
